The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
Abstract
We deal with distributed estimation of deterministic vector parameters using ad hoc wireless sensor networks (WSNs). We cast the decentralized estimation problem as the solution of multiple constrained convex optimization subproblems. Using the method of multipliers in conjunction with a block coordinate descent approach we demonstrate how the resultant algorithm can be decomposed into a set of simpler tasks suitable for distributed implementation. Different from existing alternatives, our approach does not require the centralized estimator to be expressible in a separable closed form in terms of averages, thus allowing for decentralized computation even of nonlinear estimators, including maximum likelihood estimators (MLE) in nonlinear and non-Gaussian data models. We prove that these algorithms have guaranteed convergence to the desired estimator when the sensor links are assumed ideal. Furthermore, our decentralized algorithms exhibit resilience in the presence of receiver and/or quantization noise. In particular, we introduce a decentralized scheme for least-squares and best linear unbiased estimation (BLUE) and establish its convergence in the presence of communication noise. Our algorithms also exhibit potential for higher convergence rate with respect to existing schemes. Corroborating simulations demonstrate the merits of the novel distributed estimation algorithms.
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Cites
Horn, R. A., & Johnson, C. R. (1985) · Cited by 22,203
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788
Fundamentals of Statistical Signal Processing: Estimation Theory
Sengijpta, S. K. (1995) · Technometrics · doi:10.1080/00401706.1995.10484391 · Cited by 11,076
Fundamentals of statistical signal processing: Estimation theory
Prasad, K. V. (1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)90195-3 · Cited by 7,902
Parallel and Distributed Computation: Numerical Methods
Bertsekas, D. P., & Tsitsiklis, J. N. (1989) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5,293
Fast linear iterations for distributed averaging
Xiao, L., & Boyd, S. (2004) · Systems & Control Letters · doi:10.1016/j.sysconle.2004.02.022 · Cited by 2,706
A scheme for robust distributed sensor fusion based on average consensus
Xiao, L., Boyd, S., & Lall, S. (2005) · doi:10.5555/1147685.1147698 · Cited by 1,170
Distributed average consensus with least-mean-square deviation
Xiao, L., Boyd, S., & Kim, S.-J. (2007) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2006.08.010 · Cited by 1,107
Consensus Filters for Sensor Networks and Distributed Sensor Fusion
Olfati-Saber, R., & Shamma, J. S. (2006) · Proceedings of the 44th IEEE Conference on Decision and Control · doi:10.1109/cdc.2005.1583238 · Cited by 982
A scheme for robust distributed sensor fusion based on average consensus
Xiao, L., Boyd, S., & Lall, S. (2005) · IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005. · doi:10.1109/ipsn.2005.1440896 · Cited by 713
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Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
Boyd, S. (2010) · now publishers, Inc. eBooks · doi:10.1561/9781601984616 · Cited by 12,677
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization
Shi, W., Ling, Q., Wu, G., & Yin, W. (2015) · SIAM Journal on Optimization · doi:10.1137/14096668x · Cited by 1,261
On the Linear Convergence of the ADMM in Decentralized Consensus Optimization
Shi, W., Ling, Q., Yuan, K., Wu, G., & Yin, W. (2014) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2014.2304432 · Cited by 820
On the Convergence of Decentralized Gradient Descent
Yuan, K., Ling, Q., & Yin, W. (2013) · arXiv · doi:10.48550/arxiv.1310.7063 · Cited by 635
Metadata
- Authors
- Schizas, I. D., Ribeiro, A., & Giannakis, G. B.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2008-01
- Volume
- 56
- Issue
- 1
- Pages
- 350-364
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2108970807
- Cited by
- 685
- References
- 17
- FWCI
- 47.85
- Type
- journal article
- Language
- en
- Open access
- closed
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- Schizas, I. D., Ribeiro, A., & Giannakis, G. B. (2008). Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2007.906734
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